The use of proton therapy for treating cancer has greatly increased over the past decade, mostly because of the advantageous interaction properties of proton beams. A proton beam initially deposits a relatively low dose upon entering the patient, and the deposited dose rises to a sharp maximum, known as the Bragg peak, near the end of the beam's range in the patient. The sharp Bragg peak and the finite range of the beam provide the ability to deliver a highly conformal treatment, allowing for dose escalation to the tumor and/or a reduction of exposure to the surrounding healthy tissues. However, errors in patient setup or positioning, day-to-day variations in internal anatomy, anatomical motion, changes to tumor and normal tissue in response to treatment, and other biological factors all lead to uncertainties in the exact position of the distal dose gradient within the patient. Because of uncertainties in the position of the distal falloff, standard proton treatment techniques include the use of large treatment volume expansions to ensure target coverage and to avoid any possible undershoot or overshoot of the beam into nearby critical structures. These large safety margins limit the ability to exploit the steep dose gradients at the distal edge of the Bragg peak, thus reducing the full clinical potential of proton radiation therapy. Therefore, there has been a recognized need for a method of verifying the in vivo beam range, to allow for a reduction in necessary treatment margins and to improve our ability to fully exploit the advantages of proton radiation therapy.
One method for in vivo range verification consists in measuring secondary gamma radiation emitted from the treated tissue (Bennett et al 1978, Paans et al. 1993, Min et al. 2006). During proton therapy, proton-nucleus interactions produce secondary gamma rays through two distinct methods: (1) by creating positron-emitting isotopes (11C, 15O, etc.) that produce coincident, 511 keV annihilation gammas (positron annihilation; PA), and (2) by leaving behind an intact, excited nucleus that quickly decays by emitting a characteristic prompt (CP) gamma ray, also called PG emissions. Because the excited nuclear states are quantized, excited elemental nuclei emit a characteristic CP gamma spectrum.
Many researchers are currently studying the use of PA (Litzenberg et al 1992, Parodi et al 2000, Enghardt et al 2005, Knopf et al 2008) and CP (Min et al 2006, Polf et al 2009a, Testa et al 2009) gamma emission as a method for in vivo dose range verification. In particular, studies of CP emission during proton therapy have shown that it is strongly correlated to the dose deposited in the patient (Min et al 2006, Polf et al 2009a, Polf et al 2009b, Moteabbed et al 2011) and to the composition and density of the irradiated tissues (Polf et al 2009a, Polf et al 2009b). These studies have focused on techniques for measuring the initial energy spectrum and spatial distribution of CP gammas emitted from tissues. However, because of the relatively high energies (2 MeV-15 MeV) of CP emission from tissue, the efficiency of standard gamma detectors and imagers is very low, and standard collimation techniques are ineffective for CP measurements.
These problems with the standard detectors have led several researchers to study the use of Compton camera imaging (CCI) to measure CP emission during proton irradiation. Compton cameras (CCs) are multiple detector devices (typically with one or more stages) that measure the energy deposition and position for each interaction of a gamma as it scatters in the different detectors of the camera. Because two-stage CCs have low efficiency for gammas with energy greater than about 1 MeV, Kurfess et al (2000) suggested the use of three-stage CCs that do not require the gammas to be completely absorbed. Recent work by Peterson et al (2010) and Robertson et al (2011) has shown that a three-stage CC could provide adequate detection efficiency to allow for measurement and imaging of secondary gammas (both CP and PA) from tissue during proton therapy.
A variety of approaches for reconstructing images from CC data have been studied. ML-EM (maximum-likelihood expectation-maximization) methods were introduced for CT imaging (Siddon 1985) and adapted for use with CC (Hebert et al 1990). A leading list-mode back-projection algorithm was introduced by Wilderman et al. and then improved by Mundy et al. (1998, 2010). Kim et al. compared two iterative forward-projection/back-projection algorithms and showed that these algorithms have better resolution than back-projection alone (2007). More recently, Nguyen et al. showed that the COSEM (complete data ordered subsets expectation maximization) algorithm produces qualitatively better image results when optimized using MAP (maximum a posteriori) than maximum-likelihood. The stochastic origin ensembles (SOE) algorithm, based on the Metropolis-Hastings algorithm, was originally introduced by Sitek (2008) for use in emission tomography. Andreyev et al. (2011) adapted the SOE algorithm to CCI and reconstructed list-mode data from simulated gamma sources embedded in phantoms. They compared their SOE results with the results obtained using the maximum-likelihood expectation-maximization (ML-EM) algorithm (Siddon 1985), designed for CT imaging, and found that SOE was much faster and produced images of similar resolution (Andreyev et al 2011).